Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{ebid:2022:Materials,

• author = "Ahmed Ebid and Ahmed Deifalla",
• title = "Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs",
• journal = "Materials",
• year = "2022",
• volume = "15",
• number = "8",
• pages = "Article No. 2732",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1996-1944",
• URL = "https://www.mdpi.com/1996-1944/15/8/2732",
• DOI = "doi:10.3390/ma15082732",
• abstract = "Although lightweight concrete is implemented in many mega projects to reduce the cost and improve the project’s economic aspect, research studies focus on investigating conventional normal-weight concrete. In addition, the punching shear failure of concrete slabs is dangerous and calls for precise and consistent prediction models. Thus, this current study investigates the prediction of the punching shear strength of lightweight concrete slabs. First, an extensive experimental database for lightweight concrete slabs tested under punching shear loading is gathered. Then, effective parameters are determined by applying the principles of statistical methods, namely, concrete density, columns dimensions, slab effective depth, concrete strength, flexure reinforcement ratio, and steel yield stress. Next, the manuscript presented three artificial intelligence models, which are genetic programming (GP), artificial neural network (ANN) and evolutionary polynomial regression (EPR). In addition, it provided guidance for future design code development, where the importance of each variable on the strength was identified. Moreover, it provided an expression showing the complicated inter-relation between affective variables. The novelty lies in developing three proposed models for the punching capacity of lightweight concrete slabs using three different (AI) techniques capable of accurately predicting the strength compared to the experimental database",
• notes = "also known as \cite{ma15082732}",

}

Genetic Programming entries for Ahmed M Ebid Ahmed Farouk Mohamed Hassan Deifalla

Citations